This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simulations. QoIs include species decay, product yield, and degree mixing. The ROMs are applied quantify understand how the chemical evolve over time. First, high-resolution datasets constructing gen...